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## ── Attaching packages ─────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 3.2.0 ✔ purrr 0.3.2
## ✔ tibble 2.1.3 ✔ dplyr 1.0.2
## ✔ tidyr 0.8.3 ✔ stringr 1.4.0
## ✔ readr 1.3.1 ✔ forcats 0.4.0
## Warning: package 'dplyr' was built under R version 3.6.2
## ── Conflicts ────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
SIAP <- read.csv("/Users/erikaluna/R\ Studio/msc_thesis/SIAP.csv")
one_crop <- SIAP %>%
filter(crop == "maiz") %>%
group_by(year, state) %>%
summarise(ag_yield = round(sum(production)/sum(harvested), digits = 2),
ag_prod = sum(production),
ag_planted = sum(planted),
ag_harv = sum(harvested),
ag_losses = sum(losses))
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
one_crop %>%
DT::datatable()
## Number of observations
## `summarise()` ungrouping output (override with `.groups` argument)
## Panel data A data frama with all years and all states that grow maize.
period <- tibble(rep(c(1980:2016), times = 32)) #32 states report maize production
colnames(period) <- c("year")
states <- tibble(rep(c("aguascalientes","baja california","baja california sur",
"campeche", "chiapas", "chihuahua","coahuila","colima",
"distrito federal","durango",
"guanajuato", "guerrero", "hidalgo", "jalisco",
"mexico", "michoacan", "morelos", "nayarit","nuevo leon",
"oaxaca", "puebla", "queretaro", "quintana roo",
"san luis potosi", "sinaloa", "sonora", "tabasco",
"tamaulipas", "tlaxcala","veracruz", "yucatan", "zacatecas"), times = 37))
colnames(states) <- c("state")
states <- states %>%
arrange(state)
states_period <- cbind(states, period)
Data frame for Maize
maize <- left_join(states_period, one_crop, by=c("state", "year"))
maize <- maize %>%
transform(i=as.numeric(factor(state))) %>%
transform(t=as.numeric(factor(year))) %>%
group_by(year) %>%
arrange(state)
maize %>%
DT::datatable()
## Plots ### Production
maize %>%
group_by(state) %>%
summarise(max_prod = max(ag_prod, na.rm=T),
min_prod = min(ag_prod, na.rm=T),
range_prod = max(ag_prod, na.rm=T) - min(ag_prod, na.rm=T),
sd_prod = sd(ag_prod, na.rm=T),
mean_prod = mean(ag_prod, na.rm=T),
median_prod = median(ag_prod, na.rm=T)) %>%
knitr::kable()
## `summarise()` ungrouping output (override with `.groups` argument)
| aguascalientes |
1493778.03 |
120108.00 |
1373670.03 |
421743.447 |
752373.75 |
715440.57 |
| baja california |
68442.00 |
25.00 |
68417.00 |
16092.783 |
18277.56 |
13658.00 |
| baja california sur |
97642.00 |
6634.00 |
91008.00 |
27614.561 |
37919.32 |
31540.65 |
| campeche |
464714.94 |
12839.00 |
451875.94 |
135606.273 |
175362.01 |
133041.15 |
| chiapas |
2135550.08 |
983415.00 |
1152135.08 |
282788.096 |
1461322.34 |
1460524.00 |
| chihuahua |
2319376.63 |
238326.00 |
2081050.63 |
550629.484 |
1211653.85 |
1125302.76 |
| coahuila |
937497.77 |
21696.96 |
915800.81 |
193974.135 |
184256.92 |
132786.00 |
| colima |
145185.85 |
46313.30 |
98872.55 |
21054.258 |
83773.46 |
77459.00 |
| distrito federal |
50161.00 |
9515.15 |
40645.85 |
9107.056 |
22218.15 |
23383.00 |
| durango |
2773265.28 |
194100.43 |
2579164.85 |
530611.100 |
689347.17 |
496439.00 |
| guanajuato |
2337499.61 |
383021.00 |
1954478.61 |
472100.685 |
1033940.03 |
897201.00 |
| guerrero |
1428121.17 |
331411.00 |
1096710.17 |
269719.455 |
1017910.16 |
1038965.35 |
| hidalgo |
879327.76 |
233929.00 |
645398.76 |
154093.490 |
519376.35 |
513798.00 |
| jalisco |
8337075.94 |
2420683.00 |
5916392.94 |
1453557.271 |
4451739.90 |
4053390.00 |
| mexico |
3610125.45 |
1037491.00 |
2572634.45 |
574154.477 |
2602864.79 |
2600755.36 |
| michoacan |
1935286.73 |
606327.00 |
1328959.73 |
340786.696 |
1173786.27 |
1130893.00 |
| morelos |
122714.05 |
29214.00 |
93500.05 |
20841.622 |
85761.65 |
90722.50 |
| nayarit |
439237.43 |
103011.00 |
336226.43 |
78919.814 |
219908.24 |
208767.21 |
| nuevo leon |
880313.40 |
30372.90 |
849940.50 |
176381.775 |
181841.54 |
128472.00 |
| oaxaca |
851011.00 |
220535.00 |
630476.00 |
164203.079 |
607910.18 |
645255.35 |
| puebla |
1417706.20 |
522662.00 |
895044.20 |
227714.433 |
1005639.22 |
1074593.00 |
| queretaro |
1035148.30 |
58909.00 |
976239.30 |
321939.078 |
423422.56 |
335106.00 |
| quintana roo |
67469.92 |
4160.32 |
63309.60 |
16919.277 |
31771.94 |
33769.56 |
| san luis potosi |
256366.00 |
68751.00 |
187615.00 |
39217.713 |
167479.30 |
172358.00 |
| sinaloa |
6462324.35 |
64754.00 |
6397570.35 |
1910634.563 |
2297744.80 |
2449096.00 |
| sonora |
841582.00 |
46513.00 |
795069.00 |
179725.857 |
235612.83 |
179494.74 |
| tabasco |
181556.66 |
53710.00 |
127846.66 |
29035.967 |
106481.81 |
104467.40 |
| tamaulipas |
1356220.00 |
153360.81 |
1202859.19 |
285901.402 |
612883.77 |
557779.01 |
| tlaxcala |
733642.80 |
134414.00 |
599228.80 |
168570.419 |
441059.10 |
407526.00 |
| veracruz |
1360234.55 |
569513.00 |
790721.55 |
222325.058 |
967546.76 |
966462.62 |
| yucatan |
160737.44 |
8924.00 |
151813.44 |
40031.634 |
109519.68 |
120614.85 |
| zacatecas |
2689221.34 |
221965.00 |
2467256.34 |
508583.271 |
588443.59 |
389535.00 |
maize %>%
ggplot(aes(state, ag_prod)) +
geom_boxplot() +
ylab("Production (tonnes)") +
xlab("State") +
#scale_y_continuous(labels = comma) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust= 0.5))
## Warning: Removed 68 rows containing non-finite values (stat_boxplot).

number_obs <- maize %>%
group_by(state) %>%
summarise(obs = sum(!is.na(ag_prod)))
## `summarise()` ungrouping output (override with `.groups` argument)
maize_complete <- number_obs %>%
filter(obs > 34)
maize_complete
## # A tibble: 31 x 2
## state obs
## <chr> <int>
## 1 aguascalientes 35
## 2 baja california sur 35
## 3 campeche 35
## 4 chiapas 35
## 5 chihuahua 35
## 6 coahuila 35
## 7 colima 35
## 8 distrito federal 35
## 9 durango 35
## 10 guanajuato 35
## # … with 21 more rows
maize_ts <- maize %>%
ggplot(aes(year, ag_prod)) +
geom_line()+
ylab("Production (tonnes)") +
xlab("Years") +
ggtitle("maize Production 1980 - 2016") +
theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
geom_rect(data = subset(maize, state %in% c(maize_complete$state)),
fill = NA, colour = "red", xmin = -Inf,xmax = Inf,
ymin = -Inf,ymax = Inf) +
facet_wrap(~state, scales="free_y", ncol=5)
#facet_wrap(~state, ncol=5)
maize_ts

Yield
maize %>%
group_by(state) %>%
summarise(max_yield = max(ag_yield, na.rm=T),
min_yield = min(ag_yield, na.rm=T),
range_yield = max(ag_yield, na.rm=T) - min(ag_yield, na.rm=T),
sd_yield = sd(ag_yield, na.rm=T),
mean_yield = mean(ag_yield, na.rm=T),
median_yield = median(ag_yield, na.rm=T)) %>%
knitr::kable()
## `summarise()` ungrouping output (override with `.groups` argument)
| aguascalientes |
39.87 |
2.00 |
37.87 |
7.2798377 |
11.8245714 |
10.59 |
| baja california |
38.54 |
0.98 |
37.56 |
8.5605573 |
7.2329032 |
3.65 |
| baja california sur |
7.41 |
2.64 |
4.77 |
1.3394085 |
5.2271429 |
5.24 |
| campeche |
2.74 |
0.58 |
2.16 |
0.5573117 |
1.5691429 |
1.56 |
| chiapas |
2.56 |
1.45 |
1.11 |
0.2942522 |
1.9654286 |
1.90 |
| chihuahua |
9.97 |
0.57 |
9.40 |
2.4569107 |
5.1268571 |
4.85 |
| coahuila |
18.86 |
1.07 |
17.79 |
3.7133300 |
5.1442857 |
4.61 |
| colima |
10.51 |
1.76 |
8.75 |
2.1389538 |
4.0705714 |
3.12 |
| distrito federal |
11.00 |
1.97 |
9.03 |
1.4986115 |
2.7760000 |
2.48 |
| durango |
12.23 |
1.14 |
11.09 |
2.4056917 |
3.8565714 |
3.48 |
| guanajuato |
5.90 |
1.56 |
4.34 |
1.3113085 |
3.1977143 |
2.76 |
| guerrero |
3.06 |
0.97 |
2.09 |
0.4969693 |
2.2025714 |
2.26 |
| hidalgo |
3.46 |
1.56 |
1.90 |
0.5767255 |
2.2722857 |
2.21 |
| jalisco |
10.60 |
3.16 |
7.44 |
2.0650197 |
6.1022857 |
5.63 |
| mexico |
6.51 |
2.55 |
3.96 |
1.0881238 |
4.3457143 |
4.56 |
| michoacan |
4.15 |
1.46 |
2.69 |
0.7386203 |
2.5660000 |
2.31 |
| morelos |
3.50 |
0.91 |
2.59 |
0.7066578 |
2.3077143 |
2.10 |
| nayarit |
8.78 |
1.91 |
6.87 |
1.6842245 |
3.7422857 |
2.95 |
| nuevo leon |
11.06 |
1.00 |
10.06 |
2.2887925 |
3.3737143 |
2.64 |
| oaxaca |
1.54 |
0.92 |
0.62 |
0.1389323 |
1.2651429 |
1.26 |
| puebla |
2.79 |
1.13 |
1.66 |
0.3887487 |
1.9354286 |
1.99 |
| queretaro |
15.83 |
0.95 |
14.88 |
3.1923000 |
4.7117143 |
3.86 |
| quintana roo |
0.99 |
0.26 |
0.73 |
0.1616054 |
0.6131429 |
0.60 |
| san luis potosi |
1.75 |
0.81 |
0.94 |
0.2563096 |
1.2337143 |
1.18 |
| sinaloa |
10.54 |
1.03 |
9.51 |
3.0795467 |
5.7425714 |
6.14 |
| sonora |
7.05 |
2.77 |
4.28 |
1.1458762 |
4.6897143 |
4.69 |
| tabasco |
2.35 |
1.19 |
1.16 |
0.1957906 |
1.6331429 |
1.62 |
| tamaulipas |
4.94 |
1.56 |
3.38 |
0.9109864 |
2.9554286 |
2.74 |
| tlaxcala |
5.66 |
1.69 |
3.97 |
1.3930423 |
3.4051429 |
2.69 |
| veracruz |
2.44 |
1.47 |
0.97 |
0.2583350 |
1.8357143 |
1.84 |
| yucatan |
1.71 |
0.49 |
1.22 |
0.1963811 |
0.8885714 |
0.89 |
| zacatecas |
8.98 |
0.74 |
8.24 |
1.7751668 |
2.0965714 |
1.34 |
maize %>%
ggplot(aes(state, ag_yield)) +
geom_boxplot() +
ylab("Yield (tonnes/ha)") +
xlab("State") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust= 0.5))
## Warning: Removed 68 rows containing non-finite values (stat_boxplot).

number_obs <- maize %>%
group_by(state) %>%
summarise(obs = sum(!is.na(ag_yield)))
## `summarise()` ungrouping output (override with `.groups` argument)
maize_complete <- number_obs %>%
filter(obs > 34)
maize_complete
## # A tibble: 31 x 2
## state obs
## <chr> <int>
## 1 aguascalientes 35
## 2 baja california sur 35
## 3 campeche 35
## 4 chiapas 35
## 5 chihuahua 35
## 6 coahuila 35
## 7 colima 35
## 8 distrito federal 35
## 9 durango 35
## 10 guanajuato 35
## # … with 21 more rows
maize_ts <- maize %>%
ggplot(aes(year, ag_yield)) +
geom_line()+
ylab("Yield (tonnes/ha)") +
xlab("Years") +
ggtitle("maize Yields 1980 - 2016") +
theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
geom_rect(data = subset(maize, state %in% c(maize_complete$state)),
fill = NA, colour = "red", xmin = -Inf,xmax = Inf,
ymin = -Inf,ymax = Inf) +
facet_wrap(~state, scales="free_y", ncol=5)
#facet_wrap(~state, ncol=5)
maize_ts

Area
maize %>%
group_by(state) %>%
summarise(max_area = max(ag_harv, na.rm=T),
min_area = min(ag_harv, na.rm=T),
range_area = max(ag_harv, na.rm=T) - min(ag_harv, na.rm=T),
sd_area = sd(ag_harv, na.rm=T),
mean_area = mean(ag_harv, na.rm=T),
median_area = median(ag_harv, na.rm=T)) %>%
knitr::kable()
## `summarise()` ungrouping output (override with `.groups` argument)
| aguascalientes |
107831.0 |
11862.00 |
95969.00 |
28937.729 |
68341.029 |
66869.0 |
| baja california |
18469.0 |
6.50 |
18462.50 |
4305.230 |
3838.984 |
2510.0 |
| baja california sur |
21908.0 |
1550.00 |
20358.00 |
6358.061 |
7633.316 |
4981.0 |
| campeche |
186293.0 |
22196.00 |
164097.00 |
50751.509 |
97657.640 |
104431.0 |
| chiapas |
960143.8 |
504332.00 |
455811.84 |
120143.243 |
746915.560 |
702700.0 |
| chihuahua |
467063.0 |
78410.00 |
388653.00 |
89282.790 |
257589.847 |
259377.0 |
| coahuila |
54916.0 |
14750.55 |
40165.45 |
11165.860 |
33169.454 |
32642.0 |
| colima |
43703.0 |
11920.00 |
31783.00 |
10029.121 |
24591.167 |
24109.0 |
| distrito federal |
14384.0 |
2160.00 |
12224.00 |
3184.903 |
8485.669 |
8136.0 |
| durango |
235521.0 |
94009.76 |
141511.24 |
36710.573 |
177580.652 |
181708.0 |
| guanajuato |
455055.6 |
147933.00 |
307122.58 |
77625.786 |
327466.658 |
336557.0 |
| guerrero |
513566.0 |
341752.00 |
171814.00 |
41750.341 |
457701.783 |
466473.0 |
| hidalgo |
280490.0 |
105952.00 |
174538.00 |
32671.741 |
228483.923 |
233995.2 |
| jalisco |
916264.0 |
590515.49 |
325748.51 |
68458.874 |
738114.661 |
748010.7 |
| mexico |
746816.0 |
360047.60 |
386768.40 |
80984.400 |
607378.266 |
605970.5 |
| michoacan |
561059.0 |
355787.48 |
205271.52 |
41065.500 |
458596.557 |
464983.0 |
| morelos |
57820.0 |
19452.00 |
38368.00 |
10091.074 |
38934.708 |
39360.0 |
| nayarit |
107741.0 |
43213.25 |
64527.75 |
14931.730 |
61620.539 |
57382.0 |
| nuevo leon |
94674.3 |
12611.80 |
82062.50 |
20017.335 |
52610.726 |
54303.0 |
| oaxaca |
598942.5 |
239930.00 |
359012.52 |
92999.724 |
473285.145 |
485977.3 |
| puebla |
629374.0 |
309824.31 |
319549.69 |
75510.784 |
521914.969 |
541182.0 |
| queretaro |
123355.9 |
43143.00 |
80212.90 |
23555.979 |
86984.931 |
88259.0 |
| quintana roo |
85575.0 |
9660.00 |
75915.00 |
20425.422 |
49413.715 |
49868.0 |
| san luis potosi |
218808.1 |
46456.00 |
172352.15 |
47219.335 |
143351.988 |
146118.0 |
| sinaloa |
613197.3 |
46119.00 |
567078.29 |
180695.804 |
311682.077 |
363936.0 |
| sonora |
172528.0 |
15090.00 |
157438.00 |
38179.905 |
51395.407 |
35458.0 |
| tabasco |
105023.0 |
30880.00 |
74143.00 |
18947.972 |
66039.302 |
67377.0 |
| tamaulipas |
504704.0 |
98155.25 |
406548.75 |
100911.694 |
213186.951 |
198205.0 |
| tlaxcala |
161104.0 |
73478.00 |
87626.00 |
18408.116 |
131840.288 |
131124.0 |
| veracruz |
647535.4 |
338706.00 |
308829.40 |
74599.616 |
522976.031 |
536845.0 |
| yucatan |
166785.0 |
13128.00 |
153657.00 |
40496.663 |
123131.349 |
139404.0 |
| zacatecas |
442043.0 |
101513.60 |
340529.40 |
79128.893 |
295477.558 |
297417.5 |
maize %>%
ggplot(aes(state, ag_harv)) +
geom_boxplot() +
ylab("Area (tonnes)") +
xlab("State") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust= 0.5))
## Warning: Removed 67 rows containing non-finite values (stat_boxplot).

number_obs <- maize %>%
group_by(state) %>%
summarise(obs = sum(!is.na(ag_harv)))
## `summarise()` ungrouping output (override with `.groups` argument)
maize_complete <- number_obs %>%
filter(obs > 34)
maize_complete
## # A tibble: 31 x 2
## state obs
## <chr> <int>
## 1 aguascalientes 35
## 2 baja california sur 35
## 3 campeche 35
## 4 chiapas 35
## 5 chihuahua 35
## 6 coahuila 35
## 7 colima 35
## 8 distrito federal 35
## 9 durango 35
## 10 guanajuato 35
## # … with 21 more rows
maize_ts <- maize %>%
ggplot(aes(year, ag_harv)) +
geom_line()+
ylab("Area harvested (ha)") +
xlab("Years") +
ggtitle("maize - Area Harvested (ha) 1980 - 2016") +
theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
geom_rect(data = subset(maize, state %in% c(maize_complete$state)),
fill = NA, colour = "red", xmin = -Inf,xmax = Inf,
ymin = -Inf,ymax = Inf) +
facet_wrap(~state, scales="free_y", ncol=5)
#facet_wrap(~state, ncol=5)
maize_ts

Losses
maize %>%
group_by(state) %>%
summarise(max_losses = max(ag_losses, na.rm=T),
min_losses = min(ag_losses, na.rm=T),
range_losses = max(ag_losses, na.rm=T) - min(ag_losses, na.rm=T),
sd_losses = sd(ag_losses, na.rm=T),
mean_losses = mean(ag_losses, na.rm=T),
median_losses = median(ag_losses, na.rm=T)) %>%
knitr::kable()
## `summarise()` ungrouping output (override with `.groups` argument)
| aguascalientes |
98179.0 |
838.0 |
97341.0 |
26988.5843 |
34821.7794 |
32473.500 |
| baja california |
3937.0 |
0.0 |
3937.0 |
874.2048 |
652.6129 |
327.000 |
| baja california sur |
4479.0 |
0.0 |
4479.0 |
758.1018 |
511.7162 |
295.750 |
| campeche |
102704.0 |
603.0 |
102101.0 |
24488.9392 |
20843.0917 |
11390.000 |
| chiapas |
120142.0 |
0.0 |
120142.0 |
30016.9511 |
28319.8903 |
19373.000 |
| chihuahua |
120423.0 |
0.0 |
120423.0 |
37834.4437 |
43595.8477 |
31065.000 |
| coahuila |
26530.0 |
4.5 |
26525.5 |
7538.9236 |
12510.2131 |
12368.000 |
| colima |
11297.0 |
0.0 |
11297.0 |
2560.0987 |
1852.5429 |
849.500 |
| distrito federal |
3083.0 |
0.0 |
3083.0 |
633.4964 |
334.8771 |
21.500 |
| durango |
116948.0 |
734.0 |
116214.0 |
30675.0632 |
29733.5677 |
16725.000 |
| guanajuato |
315239.0 |
5352.0 |
309887.0 |
70134.1731 |
94967.8444 |
68616.000 |
| guerrero |
118839.0 |
1971.0 |
116868.0 |
27630.4202 |
20315.9821 |
10258.250 |
| hidalgo |
147952.0 |
4178.9 |
143773.1 |
34477.2629 |
43952.7397 |
33819.850 |
| jalisco |
201237.0 |
0.0 |
201237.0 |
45142.1965 |
51988.8726 |
35475.500 |
| mexico |
284474.0 |
0.0 |
284474.0 |
56461.4675 |
31347.6224 |
13341.000 |
| michoacan |
134149.0 |
6713.0 |
127436.0 |
29750.4589 |
35081.6806 |
26622.500 |
| morelos |
36077.0 |
0.0 |
36077.0 |
6468.7874 |
2406.3403 |
133.000 |
| nayarit |
11254.0 |
0.0 |
11254.0 |
2878.9156 |
2337.1486 |
1261.000 |
| nuevo leon |
81924.0 |
404.0 |
81520.0 |
23709.7851 |
29212.2491 |
30140.000 |
| oaxaca |
187341.0 |
1010.0 |
186331.0 |
51409.7368 |
65867.4032 |
43324.500 |
| puebla |
292870.2 |
4240.0 |
288630.2 |
74611.8889 |
79630.7631 |
48354.000 |
| queretaro |
74808.4 |
0.0 |
74808.4 |
20777.9119 |
25499.7086 |
18273.000 |
| quintana roo |
64672.0 |
350.0 |
64322.0 |
19000.7413 |
21795.2380 |
17931.000 |
| san luis potosi |
152662.2 |
37590.0 |
115072.2 |
35708.2068 |
90717.3109 |
95791.500 |
| sinaloa |
416497.8 |
5046.0 |
411451.8 |
71139.0756 |
47302.9776 |
27423.500 |
| sonora |
24422.0 |
0.0 |
24422.0 |
5689.9027 |
4264.8015 |
2649.000 |
| tabasco |
34128.0 |
242.0 |
33886.0 |
8200.7811 |
8798.5000 |
5850.625 |
| tamaulipas |
88012.0 |
8466.0 |
79546.0 |
21456.4533 |
38187.3582 |
34609.150 |
| tlaxcala |
71459.0 |
0.0 |
71459.0 |
15066.7309 |
8802.8491 |
1498.000 |
| veracruz |
117580.0 |
5149.0 |
112431.0 |
29292.9785 |
54707.6020 |
49240.000 |
| yucatan |
171644.0 |
0.0 |
171644.0 |
45117.3412 |
32991.3453 |
13879.500 |
| zacatecas |
180285.0 |
1773.0 |
178512.0 |
53379.0982 |
68336.0388 |
69056.000 |
maize %>%
ggplot(aes(state, ag_losses)) +
geom_boxplot() +
ylab("Losses (ha)") +
xlab("State") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust= 0.5))
## Warning: Removed 83 rows containing non-finite values (stat_boxplot).

number_obs <- maize %>%
group_by(state) %>%
summarise(obs = sum(!is.na(ag_losses)))
## `summarise()` ungrouping output (override with `.groups` argument)
maize_complete <- number_obs %>%
filter(obs > 34)
maize_complete
## # A tibble: 16 x 2
## state obs
## <chr> <int>
## 1 campeche 35
## 2 chiapas 35
## 3 chihuahua 35
## 4 coahuila 35
## 5 colima 35
## 6 distrito federal 35
## 7 durango 35
## 8 hidalgo 35
## 9 morelos 35
## 10 nayarit 35
## 11 nuevo leon 35
## 12 puebla 35
## 13 queretaro 35
## 14 quintana roo 35
## 15 tlaxcala 35
## 16 veracruz 35
maize_ts <- maize %>%
ggplot(aes(year, ag_losses)) +
geom_line()+
ylab("Losses (ha)") +
xlab("Years") +
ggtitle("maize - Losses (planted-harvested) (ha) 1980 - 2016") +
theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
geom_rect(data = subset(maize, state %in% c(maize_complete$state)),
fill = NA, colour = "red", xmin = -Inf,xmax = Inf,
ymin = -Inf,ymax = Inf) +
facet_wrap(~state, scales="free_y", ncol=5)
#facet_wrap(~state, ncol=5)
maize_ts
